Managerial Economics Applications Strategies and Tactics 13th Edition by James R. McGuigan - Test Bank

Managerial Economics Applications Strategies and Tactics 13th Edition by James R. McGuigan – Test Bank

 

Instant Download – Complete Test Bank With Answers

 

 

Sample Questions Are Posted Below

 

Chapter 5—Business and Economic Forecasting

 

MULTIPLE CHOICE

 

1. Time-series forecasting models:

a.	are useful whenever changes occur rapidly and wildly
b.	are more effective in making long-run forecasts than short-run forecasts
c.	are based solely on historical observations of the values of the variable being forecasted
d.	attempt to explain the underlying causal relationships which produce the observed outcome
e.	none of the above

 

 

ANS:  C                    PTS:   1

 

2. The forecasting technique which attempts to forecast short-run changes and makes use of economic indicators known as leading, coincident or lagging indicators is known as:

a.	econometric technique
b.	time-series forecasting
c.	opinion polling
d.	barometric technique
e.	judgment forecasting

 

 

ANS:  D                    PTS:   1

 

3. The use of quarterly data to develop the forecasting model Y_t = a +bY_t–1 is an example of which forecasting technique?

a.	Barometric forecasting
b.	Time-series forecasting
c.	Survey and opinion
d.	Econometric methods based on an understanding of the underlying economic variables involved
e.	Input-output analysis

 

 

ANS:  B                    PTS:   1

 

4. Variations in a time-series forecast can be caused by:

a.	cyclical variations
b.	secular trends
c.	seasonal effects
d.	a and b only
e.	a, b, and c

 

 

ANS:  E                    PTS:   1

 

 

 

 

 

 

 

 

 

5. The variation in an economic time-series which is caused by major expansions or contractions usually

of greater than a year in duration is known as:

a.	secular trend
b.	cyclical variation
c.	seasonal effect
d.	unpredictable random factor
e.	none of the above

 

 

ANS:  B                    PTS:   1

 

6. The type of economic indicator that can best be used for business forecasting is the:

a.	leading indicator
b.	coincident indicator
c.	lagging indicator
d.	current business inventory indicator
e.	optimism/pessimism indicator

 

 

ANS:  A                    PTS:   1

 

7. Consumer expenditure plans is an example of a forecasting method. Which of the general categories best described this example?

a.	time-series forecasting techniques
b.	barometric techniques
c.	survey techniques and opinion polling
d.	econometric techniques
e.	input-output analysis

 

 

ANS:  C                    PTS:   1

 

8. In the first-order exponential smoothing model, the new forecast is equal to a weighted average of the old forecast and the actual value in the most recent period.

a.	true
b.	false

 

 

ANS:  A                    PTS:   1

 

9. Simplified trend models are generally appropriate for predicting the turning points in an economic time series.

a.	true
b.	false

 

 

ANS:  B                    PTS:   1

 

10. Smoothing techniques are a form of ____ techniques which assume that there is an underlying pattern to be found in the historical values of a variable that is being forecast.

a.	opinion polling
b.	barometric forecasting
c.	econometric forecasting
d.	time-series forecasting
e.	none of the above

 

 

ANS:  D                    PTS:   1

 

11. Seasonal variations can be incorporated into a time-series model in a number of different ways, including:

a.	ratio-to-trend method
b.	use of dummy variables
c.	root mean squared error method
d.	a and b only
e.	a, b, and c

 

 

ANS:  D                    PTS:   1

 

12. For studying demand relationships for a proposed new product that no one has ever used before, what would be the best method to use?
13. ordinary least squares regression on historical data
14. market experiments, where the price is set differently in two markets
15. consumer surveys, where potential customers hear about the product and are asked their opinions
16. double log functional form regression model
17. all of the above are equally useful in this case

 

ANS:  C                 PTS:  1

 

13. Which of the following barometric indicators would be the most helpful for forecasting future sales for an industry?
14. lagging economic indicators.
15. leading economic indicators.
16. coincident economic indicators.
17. wishful thinking
18. none of the above

 

ANS:  B           PTS:  1

 

14. An example of a time series data set is one for which the:
15. data would be collected for a given firm for several consecutive periods (e.g., months).
16. data would be collected for several different firms at a single point in time.
17. regression analysis comes from data randomly taken from different points in time.
18. data is created from a random number generation program.
19. use of regression analysis would impossible in time series.

 

ANS:  A            PTS: 1

 

15. Examine the plot of data.

Sales

 

©                     ©                     ©

©          ©         ©              ©         ©

©                     ©

 

 

                                                                             Time

It is likely that the best forecasting method for this plot would be:

1. a two-period moving average
2. a secular trend upward
3. a seasonal pattern that can be modeled using dummy variables or seasonal adjustments
4. a semi-log regression model
5. a cubic functional form

 

ANS:  C                       PTS:  1

 

16. Emma uses a linear model to forecast quarterly same-store sales at the local Garden Center. The results of her multiple regression is:

 

Sales = 2,800 + 200•T – 350•D

 

where T goes from 1 to 16 for each quarter of the year from the first quarter of 2006 (‘06I) through the fourth quarter of 2009 (‘09 IV).  D is a dummy variable which is 1 if sales are in the cold and dreary first quarter, and zero otherwise, because the months of January, February, and March generate few sales at the Garden Center.  Use this model to estimate sales in a store for the first quarter of 2010 in the 17^th month; that is: {2010 I}. Emma’s forecast should be:

1. 5,950
2. 6,200
3. 6,350
4. 6,000
5. 5,850

 

ANS:  E            PTS: 1

 

17. Select the correct statement.
18. Qualitative forecasts give the direction of change.
19. Quantitative forecasts give the exact amount or exact percentage change.
20. Diffusion forecasts use the proportion of the forecasts that are positive to forecast up or down.
21. Surveys are a form of qualitative forecasting.
22. all of the above are correct.

 

ANS:  E            PTS:  1

 

18. If two alternative economic models are offered, other things equal, we would
19. tend to pick the one with the lowest R^2.
20. select the model that is the most expensive to estimate.
21. pick the model that was the most complex.
22. select the model that gave the most accurate forecasts
23. all of the above

 

ANS:  D           PTS:  1

 

19. Mr. Geppetto uses exponential smoothing to predict revenue in his wood carving business. He uses a weight of w = .4 for the naïve forecast and (1-w) = .6 for the past forecast. What revenue did he predict for March using the data below? Select closet answer.

MONTH   REVENUE        FORECAST

Nov           100                        100

Dec            90                          100

Jan             115                        —-

Feb            110                        —-

      MARCH   ?                           ?

106. 106.2
107. 104.7
108. 103.2
109. 102.1
110. 101.7

ANS:  A                 PTS:  1

 

20. Suppose a plot of sales data over time appears to follow an S-shape as illustrated below.

 

Sales                                                ¨

¨

 

¨

¨

¨     ¨

 

                                                                              Time

 

Which of the following is likely that the best forecasting functional form to use for sales data above?

1. A linear trend, Sales = a + b T
2. A quadratic shape in T, using T-squared as another variable, Sales = a + b T + cT².
3. A semi-log form as sales appear to be growing at a constant percentage rate, Ln Sales = a + bT
4. A cubic shape in T, using T-squared and T-cubed as variables, Sales = a + b T + cT² + d T³.
5. A quadratic shape in T and T-squared as variables, Sales = a + b T + cT²

 

ANS:  D                 PTS: 1

 

PROBLEM

 

1. The Accuweather Corporation manufactures barometers and thermometers for weather forecasters. In an attempt to forecast its future needs for mercury, Accuweather’s chief economist estimated average monthly mercury needs as:

 

N = 500 + 10X

 

where N = monthly mercury needs (units) and X = time period in months (January 2008= 0). The following monthly seasonal adjustment factors have been estimated using data from the past five years:

 

Month	Adjustment Factor
January	15%
April	10%
July	-20%
September	5%
December	-10%

 

(a)	Forecast Accuweather’s mercury needs for January, April, July, September, and December of 2010.
(b)	The following actual and forecast values of mercury needs in the month of November have been recorded:
	Year	Actual	Forecast
	2008	456	480
	2009	324	360
	2007	240	240

 

What seasonal adjustment factor should the firm use for November?

 

ANS:

 

(a)		Unadjusted		Adjusted
	Month	Forecast		Forecast
	January 2010	500 + 10(24) = 740		740(1.15) = 851
	April 2010	500 + 10(27) = 770		770(1.10) = 847
	July 2010	500 + 10(30) = 800		800(.80) = 640
	September 2010	500 + 10(32) = 820		820(1.05) = 861
	December 2010	500 + 10(35) = 850		850(.90) = 765
(b)	Year	Actual	Forecast		Actual/Forecast
	2008	456	480		.95
	2009	324	360		.90
	2007	240	240		1.00
			Total		2.85

 

 

PTS:   1

 

 

 

 

 

 

2. Milner Brewing Company experienced the following monthly sales (in thousands of barrels) during 2010:

 

Jan.	Feb.	Mar.	Apr.	May	June
100	92	112	108	116	116

 

(a)	Develop 2-month moving average forecasts for March through July.
(b)	Develop 4-month moving average forecasts for May through July.
(c)	Develop forecasts for February through July using the exponential smoothing method (with w = .5). Begin by assuming .

 

 

ANS:

 

				(c) Exponential
	Actual	(a) 2-month	(b) 4-month	Smoothing
Month	Sales	Moving Average	Moving Average	w = .5
Jan.	100	—	—	—
Feb.	92	—	—	100
Mar.	112		—	100 + .5(82 – 100) = 96
April	108		—	96 + .5(112 – 96) = 104
May	116			104 + .5(108 – 104) = 106
June	116			106 + .5(116 – 106) = 111
July	—			111 + .5(116 – 111) = 113.5

 

 

PTS:   1